Lee S McDaniel, Nicholas C Henderson, Paul J Rathouz
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引用次数: 0
Abstract
Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, geeM, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the Matrix package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of geeM, which is not much worse than C implementations like geepack and gee on small data sets and faster on large data sets.
R JournalCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍:
The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R.
The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to:
- put their contribution in context, in particular discuss related R functions or packages;
- explain the motivation for their contribution;
- provide code examples that are reproducible.